library(tidyverse)
library(ggdist)
library(ggside)
library(easystats)
library(patchwork)
library(brms)
logmod <- function(x) sign(x) * log(1 + abs(x))
sqrtmod <- function(x) sign(x) * sqrt(abs(x))
cbrtmod <- function(x) sign(x) * (abs(x)**(1 / 3))
perceptual <- read.csv("../data/preprocessed_perceptual.csv") |>
mutate(
Block = as.factor(Block),
Illusion_Side = as.factor(Illusion_Side)
)
# Test ISI
dat <- data.frame()
for(i in c("Ebbinghaus", "MullerLyer", "VerticalHorizontal")) {
dat <- rbind(
mgcv::gamm(RT ~ s(ISI),
random = list(Participant = ~1),
data=filter(perceptual, Illusion_Type == i, Error==0)) |>
modelbased::estimate_relation(length=30) |>
mutate(Illusion_Type = i, Outcome = "RT", type="GAM"),
glmmTMB::glmmTMB(RT ~ poly(ISI, 2) + (1|Participant),
data =filter(perceptual, Illusion_Type == i, Error==0)) |>
modelbased::estimate_relation(length=30) |>
select(-Participant) |>
mutate(Illusion_Type = i, Outcome = "RT", type="poly"),
mgcv::gamm(Error ~ s(ISI),
random = list(Participant = ~1),
data=filter(perceptual, Illusion_Type == i),
family = "binomial") |>
modelbased::estimate_relation(length=30) |>
mutate(Illusion_Type = i, Outcome = "Error", type="GAM"),
glmmTMB::glmmTMB(Error ~ poly(ISI, 2) + (1|Participant),
data =filter(perceptual, Illusion_Type == i),
family = "binomial") |>
modelbased::estimate_relation(length=30) |>
select(-Participant) |>
mutate(Illusion_Type = i, Outcome = "Error", type="poly")
) |>
rbind(dat)
}
dat |>
ggplot(aes(y = Predicted, x=ISI)) +
geom_ribbon(aes(ymin=CI_low, ymax=CI_high, fill = type), alpha=0.3) +
geom_line(aes(color = type)) +
facet_wrap(Outcome ~ Illusion_Type, scales = "free")
test_models <- function(data) {
# TODO: add random effect
models_err <- list()
models_rt <- list()
for(f in c("Illusion_Difference",
"logmod(Illusion_Difference)",
"sqrtmod(Illusion_Difference)",
"cbrtmod(Illusion_Difference)")) {
err <- glmmTMB::glmmTMB(as.formula(paste0("Error ~ ", f, "+ (1|Participant)")),
data = data, family = "binomial")
models_err[[f]] <- err
rt <- glmmTMB::glmmTMB(as.formula(paste0("RT ~ ", f, " + poly(ISI, 2) + (1|Participant)")),
data = filter(data, Error == 0))
models_rt[[f]] <- rt
}
mutate(performance::test_performance(models_err), Outcome = "Error") |>
rbind(mutate(performance::test_performance(models_rt), Outcome = "RT")) |>
select(-Model, -log_BF) |>
datawizard::convert_na_to(select="BF", replacement = 1) |>
arrange(Outcome, desc(BF)) |>
export_table(footer = "Each model is compared to 'Illusion_Difference'")
}
test_models(filter(perceptual, Illusion_Type == "Ebbinghaus"))
data <- filter(perceptual, Illusion_Type == "Ebbinghaus")
plot_desc_errors <- function(data) {
data |>
ggplot(aes(x = Illusion_Difference)) +
geom_histogram(data=filter(data, Error == 1),
aes(y=..count../sum(..count..), fill = Illusion_Side),
binwidth = diff(range(data$Illusion_Difference)) / 20, color = "white") +
geom_smooth(aes(y = Error, color = Illusion_Side),
method = 'gam',
formula = y ~ s(x, bs = "cs"),
method.args = list(family = "binomial")) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
scale_color_manual(values = c("-1" = "#FF5722", "1" = "#43A047")) +
scale_fill_manual(values = c("-1" = "#FF5722", "1" = "#43A047")) +
coord_cartesian(ylim = c(0, 1), xlim = range(data$Illusion_Difference)) +
labs(x = "Task Difficulty", y = "Probability of Error") +
theme_modern()
}
plot_desc_errors(data)
formula <- brms::bf(
Error ~ sqrtmod(Illusion_Difference) +
(1 + sqrtmod(Illusion_Difference) | Participant),
family = "bernoulli",
decomp = "QR"
)
# brms::get_prior(formula, data = data)
# brms::validate_prior(formula)
perceptual_ebbinghaus_err <- brms::brm(formula,
data = data,
refresh = 0,
normalize = FALSE
)
save(perceptual_ebbinghaus_err, file="models/perceptual_ebbinghaus_err.Rdata")
load("models/perceptual_ebbinghaus_err.Rdata")
plot_model_errors <- function(data, model) {
pred <- estimate_relation(model, at="Illusion_Difference", length = 100)
data |>
ggplot(aes(x = Illusion_Difference)) +
geom_histogram(data=filter(data, Error == 1),
aes(y=..count../sum(..count..)),
binwidth = diff(range(data$Illusion_Difference)) / 20) +
geom_ribbon(data = pred,
aes(ymin = CI_low, ymax = CI_high),
alpha = 1/3, fill = "red") +
geom_line(data = pred,
aes(y = Predicted),
color = "red") +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0), labels = scales::percent) +
coord_cartesian(ylim = c(0, 1), xlim = range(data$Illusion_Difference)) +
labs(x = "Task Difficulty", y = "Probability of Error") +
theme_modern()
}
plot_model_errors(data, perceptual_ebbinghaus_err)
performance::performance(perceptual_ebbinghaus_err, metrics = c("R2", "ICC")) |>
display()
| R2 | R2 (marg.) | ICC |
|---|---|---|
| 0.09 | 0.02 | 0.06 |
data <- filter(perceptual, Illusion_Type == "Ebbinghaus", Error == 0)
plot_desc_rt <- function(data) {
data |>
ggplot(aes(x = Illusion_Difference, y = RT)) +
# ggpointdensity::geom_pointdensity(size = 3, alpha=0.5) +
# scale_color_gradientn(colors = c("grey", "black"), guide = "none") +
# ggnewscale::new_scale_color() +
stat_density_2d(aes(fill = ..density..), geom = "raster", contour = FALSE) +
scale_fill_gradientn(colors = c("white", "black"), guide = "none") +
ggnewscale::new_scale_fill() +
geom_smooth(aes(color = Illusion_Side, fill = Illusion_Side),
method = 'gam',
formula = y ~ s(x, bs = "cs")) +
scale_color_manual(values = c("-1" = "#FF5722", "1" = "#43A047")) +
scale_fill_manual(values = c("-1" = "#FF5722", "1" = "#43A047")) +
scale_x_discrete(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Task Difficulty", y = "Response Time (s)") +
coord_cartesian(ylim = c(0, 2.5)) +
theme_modern() +
ggside::geom_ysidedensity(aes(fill = Illusion_Side), color = NA, alpha = 0.3) +
ggside::theme_ggside_void() +
ggside::scale_ysidex_continuous(expand = c(0, 0)) +
ggside::ggside()
}
plot_desc_rt(data)
# TODO: Add random to parameters
formula <- brms::bf(
RT ~ sqrtmod(Illusion_Difference) + poly(ISI, 2) +
(1 + sqrtmod(Illusion_Difference)| Participant),
sigma ~ sqrtmod(Illusion_Difference) + poly(ISI, 2) +
(1 + sqrtmod(Illusion_Difference)| Participant),
beta ~ sqrtmod(Illusion_Difference) + poly(ISI, 2) +
(1 + sqrtmod(Illusion_Difference)| Participant),
family = "exgaussian",
decomp = "QR"
)
# brms::get_prior(formula, data = data)
# brms::validate_prior(formula)
perceptual_ebbinghaus_rt <- brms::brm(formula,
data = data,
refresh = 0,
init = 0,
normalize = FALSE
)
save(perceptual_ebbinghaus_rt, file="models/perceptual_ebbinghaus_rt.Rdata")
load("models/perceptual_ebbinghaus_rt.Rdata")
plot_model_rt <- function(data, model) {
pred <- estimate_relation(model, at="Illusion_Difference", length = 100)
data |>
ggplot(aes(x = Illusion_Difference)) +
stat_density_2d(aes(fill = ..density.., y = RT), geom = "raster", contour = FALSE) +
scale_fill_gradientn(colors = c("white", "black"), guide = "none") +
ggnewscale::new_scale_fill() +
geom_ribbon(data = pred,
aes(ymin = CI_low, ymax = CI_high),
alpha = 1/3, fill = "red") +
geom_line(data = pred,
aes(y = Predicted),
color = "red") +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
labs(x = "Task Difficulty", y = "Response Time (s)") +
coord_cartesian(ylim = c(0, 2.5)) +
theme_modern()
}
plot_model_rt(data, perceptual_ebbinghaus_rt)
performance::performance(perceptual_ebbinghaus_rt, metrics = c("R2", "ICC")) |>
display()
| R2 | R2 (marg.) |
|---|---|
| 0.43 | 0.02 |
plot_ppcheck <- function(model) {
pred <- modelbased::estimate_prediction(model, keep_iterations = 50) |>
bayestestR::reshape_iterations() |>
mutate(iter_group = as.factor(iter_group)) |>
estimate_density(select = "iter_value", at = "iter_group")
estimate_density(insight::get_data(model)$RT) |>
ggplot(aes(x = x, y = y)) +
geom_area(fill = "#9E9E9E") +
geom_line(
data = pred,
aes(group = iter_group), color = "#FF5722", size = 0.1, alpha = 0.5
) +
scale_y_continuous(expand = c(0, 0)) +
scale_x_continuous(expand = c(0, 0)) +
coord_cartesian(xlim = c(0, 2)) +
theme_modern() +
labs(x = "Reaction Time (ms)", y = "", title = "Posterior Predictive Check") +
theme(
plot.title = element_text(face = "bold", hjust = 0.5),
axis.text.y = element_blank()
)
}
plot_ppcheck(perceptual_ebbinghaus_rt)
test_models(filter(perceptual, Illusion_Type == "MullerLyer") )
data <- filter(perceptual, Illusion_Type == "MullerLyer")
plot_desc_errors(data)
formula <- brms::bf(
Error ~ cbrtmod(Illusion_Difference) +
(1 + cbrtmod(Illusion_Difference) | Participant),
family = "bernoulli",
decomp = "QR"
)
# brms::get_prior(formula, data = data)
# brms::validate_prior(formula)
perceptual_mullerlyer_err <- brms::brm(formula,
data = data,
refresh = 0,
normalize = FALSE
)
save(perceptual_mullerlyer_err, file="models/perceptual_mullerlyer_err.Rdata")
load("models/perceptual_mullerlyer_err.Rdata")
plot_model_errors(data, perceptual_mullerlyer_err)
performance::performance(perceptual_mullerlyer_err, metrics = c("R2", "ICC")) |>
display()
| R2 | R2 (marg.) | ICC |
|---|---|---|
| 0.10 | 0.05 | 0.24 |
data <- filter(perceptual, Illusion_Type == "MullerLyer", Error == 0)
plot_desc_rt(data)
# TODO: Add random to parameters
formula <- brms::bf(
RT ~ cbrtmod(Illusion_Difference) + poly(ISI, 2) +
(1 + cbrtmod(Illusion_Difference) | Participant),
sigma ~ cbrtmod(Illusion_Difference) + poly(ISI, 2) +
(1 + cbrtmod(Illusion_Difference) | Participant),
beta ~ cbrtmod(Illusion_Difference) + poly(ISI, 2) +
(1 + cbrtmod(Illusion_Difference) | Participant),
family = "exgaussian",
decomp = "QR"
)
# brms::get_prior(formula, data = data)
# brms::validate_prior(formula)
perceptual_mullerlyer_rt <- brms::brm(formula,
data = data,
refresh = 0,
init = 0,
normalize = FALSE
)
save(perceptual_mullerlyer_rt, file="models/perceptual_mullerlyer_rt.Rdata")
load("models/perceptual_mullerlyer_rt.Rdata")
plot_model_rt(data, perceptual_mullerlyer_rt)
performance::performance(perceptual_mullerlyer_rt, metrics = c("R2", "ICC")) |>
display()
| R2 | R2 (marg.) |
|---|---|
| 0.43 | 0.05 |
plot_ppcheck(perceptual_mullerlyer_rt)
test_models(filter(perceptual, Illusion_Type == "VerticalHorizontal") )
data <- filter(perceptual, Illusion_Type == "VerticalHorizontal")
plot_desc_errors(data)
formula <- brms::bf(
Error ~ cbrtmod(Illusion_Difference) +
(1 + cbrtmod(Illusion_Difference) | Participant),
family = "bernoulli",
decomp = "QR"
)
# brms::get_prior(formula, data = data)
# brms::validate_prior(formula)
perceptual_verticalhorizontal_err <- brms::brm(formula,
data = data,
refresh = 0,
normalize = FALSE
)
save(perceptual_verticalhorizontal_err, file="models/perceptual_verticalhorizontal_err.Rdata")
load("models/perceptual_verticalhorizontal_err.Rdata")
plot_model_errors(data, perceptual_verticalhorizontal_err)
performance::performance(perceptual_verticalhorizontal_err, metrics = c("R2", "ICC")) |>
display()
| R2 | R2 (marg.) | ICC |
|---|---|---|
| 0.10 | 0.05 | 0.30 |
data <- filter(perceptual, Illusion_Type == "VerticalHorizontal", Error == 0)
plot_desc_rt(data)
# TODO: Add random to parameters
formula <- brms::bf(
RT ~ logmod(Illusion_Difference) + poly(ISI, 2) +
(1 + logmod(Illusion_Difference) | Participant),
sigma ~ logmod(Illusion_Difference) + poly(ISI, 2) +
(1 + logmod(Illusion_Difference) | Participant),
beta ~ logmod(Illusion_Difference) + poly(ISI, 2) +
(1 + logmod(Illusion_Difference) | Participant),
family = "exgaussian",
decomp = "QR"
)
# brms::get_prior(formula, data = data)
# brms::validate_prior(formula)
perceptual_verticalhorizontal_rt <- brms::brm(formula,
data = data,
refresh = 0,
init = 0,
normalize = FALSE
)
save(perceptual_verticalhorizontal_rt, file="models/perceptual_verticalhorizontal_rt.Rdata")
load("models/perceptual_verticalhorizontal_rt.Rdata")
plot_model_rt(data, perceptual_verticalhorizontal_rt)
performance::performance(perceptual_verticalhorizontal_rt, metrics = c("R2", "ICC")) |>
display()
| R2 | R2 (marg.) |
|---|---|
| 0.46 | 0.03 |
plot_ppcheck(perceptual_verticalhorizontal_rt)
get_scores <- function(model, illusion="Ebbinghaus") {
family <- insight::find_response(model)
scores <- modelbased::estimate_grouplevel(model) |>
data_filter(str_detect(Level, "Participant")) |>
mutate(Level = str_remove(Level, "Participant."),
Level = str_remove(Level, "_beta."),
Level = str_remove(Level, "_sigma."),
Group = str_remove(Group, ": Participant"),
Group = str_remove_all(Group, "cbrtmod"),
Group = str_remove_all(Group, "sqrtmod"),
Group = str_remove_all(Group, "sqrt"),
Group = str_remove_all(Group, "logmod"),
Group = str_remove_all(Group, "abs"),
Group = str_replace(Group, "Illusion_Difference", "Diff"),
Group = str_replace(Group, "Intercept__sigma", "InterceptSigma"),
Group = str_replace(Group, "Intercept__beta", "InterceptBeta"),
Group = str_replace(Group, "Diff__sigma", "DiffSigma"),
Group = str_replace(Group, "Diff__beta", "DiffBeta")) |>
mutate(Parameter = case_when(str_detect(Parameter, "Intercept") ~ "Intercept",
str_detect(Parameter, "Illusion_Difference") ~ "Diff",
TRUE ~ "PROBLEM"),
Group = str_remove(str_remove(Group, "__"), "Participant"),
Group = paste0(Parameter, tools::toTitleCase(Group)))
colors <- c("Diff" = "#210c4a", "Intercept" = "#e45a31",
"DiffBeta" = "#57106e", "InterceptBeta" = "#f98e09",
"DiffSigma" = "#8a226a", "InterceptSigma" = "#f9cb35")
p <- scores |>
ggplot(aes(x = Median, y = Level)) +
geom_pointrange(aes(xmin = CI_low, xmax = CI_high, color = Group), linewidth=0.25) +
scale_color_manual(values = colors, guide = "none") +
scale_fill_manual(values = colors, guide = "none") +
labs(y = "Participants") +
theme_modern() +
theme(strip.placement = "oustide",
axis.title.x = element_blank(),
axis.text.y = element_blank()) +
ggside::geom_xsidedensity(aes(fill=Group, y = after_stat(scaled)), color = NA, alpha = 0.3) +
ggside::theme_ggside_void() +
ggside::scale_xsidey_continuous(expand = c(0, 0)) +
ggside::ggside() +
facet_grid(~Group, switch = "both", scales = "free") +
ggtitle(paste(illusion, "-", family))
scores <- scores |>
select(Group, Participant = Level, Median) |>
pivot_wider(names_from = "Group", values_from = "Median") |>
data_rename("Diff",
paste0("Perception_", illusion, "_Difficulty_", family), verbose=FALSE) |>
data_rename("DiffSigma",
paste0("Perception_", illusion, "_Difficulty_SigmaRT"), verbose=FALSE) |>
data_rename("DiffBeta",
paste0("Perception_", illusion, "_Difficulty_BetaRT"), verbose=FALSE) |>
data_rename("Intercept",
paste0("Perception_", illusion, "_Intercept_", family), verbose=FALSE) |>
data_rename("InterceptSigma",
paste0("Perception_", illusion, "_Intercept_SigmaRT"), verbose=FALSE) |>
data_rename("InterceptBeta",
paste0("Perception_", illusion, "_Intercept_BetaRT"), verbose=FALSE)
list(scores = scores, p = p)
}
ebbinghaus_err <- get_scores(perceptual_ebbinghaus_err, illusion="Ebbinghaus")
ebbinghaus_rt <- get_scores(perceptual_ebbinghaus_rt, illusion="Ebbinghaus")
mullerlyer_err <- get_scores(perceptual_mullerlyer_err, illusion="MullerLyer")
mullerlyer_rt <- get_scores(perceptual_mullerlyer_rt, illusion="MullerLyer")
verticalhorizontal_err <- get_scores(perceptual_verticalhorizontal_err, illusion="VerticalHorizontal")
verticalhorizontal_rt <- get_scores(perceptual_verticalhorizontal_rt, illusion="VerticalHorizontal")
p <- (
(ebbinghaus_err$p + ebbinghaus_rt$p +
plot_layout(guides = "collect", widths = c(0.3, 0.7))) /
(mullerlyer_err$p + mullerlyer_rt$p +
plot_layout(guides = "collect", widths = c(0.3, 0.7))) /
(verticalhorizontal_err$p + verticalhorizontal_rt$p +
plot_layout(guides = "collect", widths = c(0.3, 0.7)))
) +
plot_annotation(title = "Inter- and Intra- Variability of Perceptual Scores", theme = theme(plot.title = element_text(face = "bold", hjust = 0.5)))
p
scores <- ebbinghaus_err$scores |>
merge(ebbinghaus_rt$scores, by="Participant") |>
merge(mullerlyer_err$scores, by="Participant") |>
merge(mullerlyer_rt$scores, by="Participant") |>
merge(verticalhorizontal_err$scores, by="Participant") |>
merge(verticalhorizontal_rt$scores, by="Participant")
write.csv(scores, "../data/scores_perceptual.csv", row.names = FALSE)